Why scaling intelligent automation requires financial rigour

The Hidden Cost Trap That’s Killing Your Automation Dreams

The “build it and they will come” mentality has been the downfall of countless automation initiatives. Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, is sounding the alarm: without financial rigor, your intelligent automation projects are destined to fail spectacularly when they hit production scale.

“When we integrate FinOps capabilities with automation, we’re looking at a change from being very reactive on cost management to being very proactive around value engineering,” Holmes reveals. This isn’t just financial jargon—it’s the difference between automation success and expensive failure.

The Brutal Reality of Scaling Automation

Here’s the uncomfortable truth: approximately 80% of new innovation projects fail. Why? Because financial opacity during the pilot phase masks future liabilities that only become apparent when you try to scale.

Holmes paints a vivid picture: “If a pilot demonstrates that automating a process saves, say, 100 hours a month, leadership thinks that’s really successful. But what it fails to track is that the pilot sometimes is running on over-provisioned infrastructure, so it looks like it performs really well. But you wouldn’t over-provision to that degree during a real production rollout.”

The moment you move that workload to production, everything changes. Compute requirements spike, storage needs balloon, data transfer costs multiply, and those “edge cases” that were rare in the pilot suddenly become your new normal. Support overheads grow exponentially, and suddenly your ROI model looks like wishful thinking.

The Unit Economics Wake-Up Call

Successful scaling requires tracking marginal costs at scale. This means monitoring unit economics like cost per customer served or cost per transaction. If your cost per customer increases as your customer base grows, you don’t have a scalable business model—you have a money pit.

Effective scaling should see these unit costs decrease. Holmes cites Liberty Mutual’s success story: the insurer found approximately $2.5 million in savings by bringing in consumption metrics and “not just looking at labor hours that they were saving.”

But here’s the game-changer: financial accountability can’t just sit with the finance department anymore. Holmes advocates putting governance “back in the hands of the developers into their development tools and workloads.”

By integrating with infrastructure-as-code tools like HashiCorp Terraform and GitHub, organizations can enforce policies during deployment. Teams can spin up resources programmatically with immediate cost estimates. “Rather than deploying things and then fixing them up, which gets into the whole whack-a-mole kind of problem,” Holmes explains, companies can verify they are “deploying the right things at the right time.”

Breaking Down the CFO vs. Automation Head War

Tension often simmers between the CFO, who focuses on return on investment, and the Head of Automation, who tracks operational metrics like hours saved. “This translation challenge is precisely what TBM (Technology Business Management) and Apptio are designed to solve,” says Holmes. “It’s having a common language between technology and finance and with the business.”

The TBM taxonomy provides a standardized framework to reconcile these views. It maps technical resources (such as compute, storage, and labor) into IT towers and further up to business capabilities. This structure translates technical inputs into business outputs.

“I don’t necessarily know what goes into all the IT layers underneath it,” Holmes says, describing the business user’s perspective. “But because we’ve got this taxonomy, I can get a detailed bill that tells me about my service consumption and precisely which costs are driving it to be more expensive as I consume more.”

Legacy Systems: Automation Patch or Bridge to Modernization?

Organizations burdened by legacy ERP systems face a binary choice: automation as a patch, or as a bridge to modernization. Holmes warns that if a company is “just trying to mask inefficient processes and not redesign them,” they are merely “building up more technical debt.”

A total cost of ownership (TCO) approach helps determine the correct strategy. The Commonwealth Bank of Australia utilized a TCO model across 2,000 different applications—of various maturity stages—to assess their full lifecycle costs. This analysis included hidden costs such as infrastructure, labor, and the engineering time required to keep automation running.

“Just because of something’s legacy doesn’t mean you have to retire it,” says Holmes. “Some of those legacy systems are worth maintaining just because the value is so good.”

In other cases, calculating the cost of the automation wrappers required to keep an old system functional reveals a different reality. “Sometimes when you add up the TCO approach, and you’re including all these automation layers around it, you suddenly realize, the real cost of keeping that old system alive is not just the old system, it’s those extra layers,” Holmes argues.

The Budgeting Strategy That Prevents Sticker Shock

Avoiding financial surprises requires a budgeting strategy that balances variable costs with long-term commitments. While variable costs (OPEX) offer flexibility, they can fluctuate wildly based on demand and engineering efficiency.

Holmes advises that longer-term visibility enables better investment decisions. Committing to specific technologies or platforms over a multi-year horizon allows organizations to negotiate economies of scale and standardize architecture.

“Because you’ve made those longer term commitments and you’ve standardized on different platforms and things like that, it makes it easier to build the right thing out for the long term,” Holmes says.

Combining tight management of variable costs with strategic commitments supports enterprises in scaling intelligent automation without the volatility that often derails transformation.

The Bottom Line on Automation ROI

The message is clear: successful scaling of intelligent automation requires financial rigor from day one. It’s not enough to prove a concept works in a controlled pilot environment. You need to understand the true unit economics, implement proper governance, speak a common language between finance and technology, and make strategic decisions about legacy systems.

Without this financial foundation, your automation initiatives will join the 80% that fail—not because the technology doesn’t work, but because the economics don’t scale.

Greg Holmes and other experts will be sharing their insights during the Intelligent Automation Conference Global in London on 4-5 February 2026. Be sure to check out the day one panel session, “Scaling Intelligent Automation Successfully: Frameworks, Risks, and Real-World Lessons,” to hear more from Holmes and swing by IBM’s booth at stand #362.

Tags: automation ROI, scaling intelligent automation, FinOps, TBM taxonomy, unit economics, legacy systems, total cost of ownership, infrastructure-as-code, financial rigor, automation governance, production scaling, cost per transaction, technical debt, enterprise automation, automation failure rates

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